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>> ISACA AAISM PDF Question <<
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NEW QUESTION # 352
A large financial services organization is integrating a third-party AI solution into its critical fraud detection system. Which of the following is the BEST way for the organization to reduce risk associated with AI vendor and supply chain dependencies?
Answer: D
Explanation:
AAISM emphasizes supplier assurance through contractual obligations as the foundational control for AI supply chain risk. Contracts should require verifiable evidence of secure development practices (e.g., secure SDLC, model and data provenance documentation, SBOM/MBOM where applicable, vulnerability disclosure, patch SLAs, audit rights, incident notification, and regulatory compliance assertions). This creates enforceable, continuous assurance beyond point-in-time tests.
NEW QUESTION # 353
An organization is considering a Software as a Service (SaaS)-based HR system that uses AI to automate resume vetting and pre-screening of candidates. Which of the following controls is MOST important to review?
Answer: C
Explanation:
AAISM states that HR systems performing candidate evaluation must prioritize training data fairness, representativeness, and bias mitigation because biased HR decisions carry regulatory, ethical, and litigation risks.
NEW QUESTION # 354
Which of the following actions BEST enables the evaluation of bias during an AI impact assessment?
Answer: B
Explanation:
In AI Security Management, bias evaluation is primarily a data problem before it is a model or performance problem. The official AI Security Management content explains that impact assessments must specifically analyze training data representativeness against the demographics and characteristics of all relevant users and stakeholders. If the data used to train an AI system underrepresents or omits particular groups, the resulting model will systematically disadvantage them, regardless of how fast or "accurate" it is on average. While comparing outputs to historical data (option B) can help, historical data itself may be biased. Performance- related options (C and D) relate to efficiency and scalability, not fairness. Therefore, systematically assessing whether the training data covers all relevant end-user groups is the most direct and effective way to evaluate and mitigate bias.
References: AI Security Management™ (AAISM) Study Guide - AI Risk Identification and Impact Assessment; Data Governance and Bias Section.
NEW QUESTION # 355
Who is responsible for implementing recommendations in a final report after an external AI compliance audit?
Answer: D
Explanation:
AAISM clarifies that model owners hold responsibility for ensuring corrective actions are implemented after AI audits. They are accountable for:
* model behavior
* compliance gaps
* security improvements
* governance alignment
Internal auditors (B) perform assessments but do not implement changes. System architects (A) support technical fixes but do not own compliance. End users (C) are not responsible for audit remediation.
References: AAISM Study Guide - Roles & Responsibilities; AI Ownership and Accountability.
NEW QUESTION # 356
Which of the following BEST enables an organization to strengthen information security controls around the use of generative AI applications?
Answer: B
Explanation:
For generative AI, the primary enterprise security exposure is data and content exfiltration or policy violations at output, including leakage of sensitive data, toxic content, or regulatory non-compliance. AAISM prescribes policy-aligned output monitoring (e.g., DLP checks, PII/PHI detection, toxicity/safety filters, watermark
/attribution checks) integrated into inference gateways to enforce organizational policies and evidence compliance. Exceeding benchmarks (A) is not a control; training-data validation (C) may be infeasible with third-party LLMs; and kill switches (D) are essential contingency controls but do not continuously strengthen everyday security posture.
References: AI Security Management™ (AAISM) Body of Knowledge - GenAI Governance and Guardrails; Output Filtering and DLP Controls; Policy Enforcement at Inference. AAISM Study Guide - Monitoring & Auditing of GenAI; Gateway Patterns for Safe Use; Control Effectiveness Measures.
NEW QUESTION # 357
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